diff --git a/paddlespeech/s2t/models/wav2vec2/modules/VanillaNN.py b/paddlespeech/s2t/models/wav2vec2/modules/VanillaNN.py
index 82313c330..9c88796bb 100644
--- a/paddlespeech/s2t/models/wav2vec2/modules/VanillaNN.py
+++ b/paddlespeech/s2t/models/wav2vec2/modules/VanillaNN.py
@@ -46,7 +46,7 @@ class VanillaNN(containers.Sequential):
                  dnn_neurons=512,
                  activation=True,
                  normalization=False,
-                 dropout_rate=0.0):
+                 dropout_rate=0.5):
         super().__init__(input_shape=[None, None, input_shape])
 
         if not isinstance(dropout_rate, list):
@@ -68,6 +68,5 @@ class VanillaNN(containers.Sequential):
             if activation:
                 self.append(paddle.nn.LeakyReLU(), layer_name="act")
             self.append(
-                paddle.nn.Dropout(),
-                p=dropout_rate[block_index],
+                paddle.nn.Dropout(p=dropout_rate[block_index]),
                 layer_name='dropout')
diff --git a/paddlespeech/s2t/models/wav2vec2/modules/normalization.py b/paddlespeech/s2t/models/wav2vec2/modules/normalization.py
new file mode 100644
index 000000000..912981058
--- /dev/null
+++ b/paddlespeech/s2t/models/wav2vec2/modules/normalization.py
@@ -0,0 +1,97 @@
+# Authors
+#  * Mirco Ravanelli 2020
+#  * Guillermo Cámbara 2021
+#  * Sarthak Yadav 2022
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from speechbrain(https://github.com/speechbrain/speechbrain/blob/develop/speechbrain/nnet/normalization.py)
+import paddle.nn as nn
+
+from paddlespeech.s2t.modules.align import BatchNorm1D
+
+
+class BatchNorm1d(nn.Layer):
+    """Applies 1d batch normalization to the input tensor.
+    Arguments
+    ---------
+    input_shape : tuple
+        The expected shape of the input. Alternatively, use ``input_size``.
+    input_size : int
+        The expected size of the input. Alternatively, use ``input_shape``.
+    eps : float
+        This value is added to std deviation estimation to improve the numerical
+        stability.
+    momentum : float
+        It is a value used for the running_mean and running_var computation.
+    affine : bool
+        When set to True, the affine parameters are learned.
+    track_running_stats : bool
+        When set to True, this module tracks the running mean and variance,
+        and when set to False, this module does not track such statistics.
+    combine_batch_time : bool
+        When true, it combines batch an time axis.
+    Example
+    -------
+    >>> input = paddle.randn([100, 10])
+    >>> norm = BatchNorm1d(input_shape=input.shape)
+    >>> output = norm(input)
+    >>> output.shape
+    Paddle.Shape([100, 10])
+    """
+
+    def __init__(
+            self,
+            input_shape=None,
+            input_size=None,
+            eps=1e-05,
+            momentum=0.9,
+            combine_batch_time=False,
+            skip_transpose=False, ):
+        super().__init__()
+        self.combine_batch_time = combine_batch_time
+        self.skip_transpose = skip_transpose
+
+        if input_size is None and skip_transpose:
+            input_size = input_shape[1]
+        elif input_size is None:
+            input_size = input_shape[-1]
+
+        self.norm = BatchNorm1D(input_size, momentum=momentum, epsilon=eps)
+
+    def forward(self, x):
+        """Returns the normalized input tensor.
+        Arguments
+        ---------
+        x : paddle.Tensor (batch, time, [channels])
+            input to normalize. 2d or 3d tensors are expected in input
+            4d tensors can be used when combine_dims=True.
+        """
+        shape_or = x.shape
+        if self.combine_batch_time:
+            if x.ndim == 3:
+                x = x.reshape(shape_or[0] * shape_or[1], shape_or[2])
+            else:
+                x = x.reshape(shape_or[0] * shape_or[1], shape_or[3],
+                              shape_or[2])
+
+        elif not self.skip_transpose:
+            x = x.transpose([0, 2, 1])
+
+        x_n = self.norm(x)
+        if self.combine_batch_time:
+            x_n = x_n.reshape(shape_or)
+        elif not self.skip_transpose:
+            x_n = x_n.transpose([0, 2, 1])
+
+        return x_n